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Continuous efforts to develop a lightweight alloy suitable for the most demanding applications in automotive industry resulted in a number of advanced aluminum (Al) and magnesium alloys and manufacturing routes. One example of this is the application of 319 Al alloy for production of 3.6L V6 gasoline engine blocks. Aluminum is sand cast around Fe-liner cylinder inserts, prior to undergoing the T7 heat treatment process. One of the critical factors determining the quality of the final product is the type, level, and profile of residual stresses along the Fe liners (or extent of liner distortion) that are always present in a cast component. In this study, neutron diffraction was used to characterize residual stresses along the Al and the Fe liners in the web region of the cast engine block. The strains were measured both in Al and Fe in hoop, radial, and axial orientations. The stresses were subsequently determined using generalized Hooke's law.

Neutron diffraction is a powerful tool that can be used to identify the phases present and to measure the spacing of the atomic planes in a material. Thus, the residual stresses can be determined within a component and/or the phases present. New intercritically austempered irons rely on the unique properties of the austenite phase present in their microstructures. If these materials are to see widespread use, methods to verify the quality (behavior consistency) of these materials and to provide guidance for further optimization will be needed. Neutron diffraction studies were performed at the second generation neutron residual stress facility (NRSF2) at the High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory on a variety of intercritically austempered irons. For similar materials, such as TRIP steels, the strengthening mechanism involves the transformation of metastable austenite to martensite during deformation.

A transient, one-dimensional lean NOx trap (LNT) model is described and implemented for vehicle systems simulations. The model accounts for conservation of chemical species and thermal energy, and includes the effects of O₂ storage and NOx storage (in the form of nitrites and nitrates). Nitrites and nitrates are formed by diffusion of NO and NO₂, respectively, into sorbent particles, and reaction rates are controlled by chemical kinetics and solid-phase diffusion. The model also accounts for thermal aging and sulfation by means of empirical correlations, which have been derived from laboratory experiments. Example simulation results using the Powertrain Systems Analysis Toolkit (PSAT) are presented.

Plug-in hybrid electric vehicle (PHEV) technologies have the potential for considerable petroleum consumption reductions, possibly at the expense of increased tailpipe emissions due to multiple “cold” start events and improper use of the engine for PHEV specific operation. PHEVs operate predominantly as electric vehicles (EVs) with intermittent assist from the engine during high power demands. As a consequence, the engine can be subjected to multiple cold start events. These cold start events may have a significant impact on the tailpipe emissions due to degraded catalyst performance and starting the engine under less than ideal conditions. On current hybrid electric vehicles (HEVs), the first cold start of the engine dictates whether or not the vehicle will pass federal emissions tests. PHEV operation compounds this problem due to infrequent, multiple engine cold starts.

Transport Canada, through its ecoTECHNOLOGY for Vehicles program, retained the services of the National Research Council Canada to undertake a test program to examine the operational and human factors considerations concerning the removal of the side mirrors on a Class 8 tractor equipped with a 53 foot dry van semi-trailer. Full scale aerodynamic testing was performed in a 2 m by 3 m wind tunnel on a system component basis to quantify the possible fuel savings associated with the removal of the side mirrors. The mirrors on a Volvo VN780 tractor were removed and replaced with a prototype camera-based indirect vision system consisting of four cameras mounted in the front fender location; two cameras on either side of the vehicle. Four monitors mounted in the vehicle - two mounted on the right A-pillar and two mounted on the left A-pillar - provided indirect vision information to the vehicle operator.

The 9-meter wind tunnel of the National Research Council (NRC) of Canada is equipped with a boundary layer suction system, center belt and wheel rollers to simulate ground motion relative to test articles. Although these systems were originally commissioned for testing of full-scale automotive models, they are appropriately sized for ground simulation with half-scale tractor-trailer combinations. The size of the tunnel presents an opportunity to test half-scale commercial vehicles at full-scale Reynolds numbers with a model that occupies 3% of the test section cross-sectional area. This study looks at the effects of ground simulation on the force and pressure data of a half-scale model with rotating tractor wheels. A series of model changes, typical of a drag reduction program, were undertaken and each configuration was tested with both a fixed floor and with full-ground simulation to evaluate the effects of this technology on the total and incremental drag coefficients.

Terrestrial winds play an important role in affecting the aerodynamics of road vehicles. Of increasing importance is the effect of the unsteady turbulence structure of these winds and their influence on the process of optimizing aerodynamic performance to reduce fuel consumption. In an effort to predict better the aerodynamic performance of heavy-duty vehicles and various drag reduction technologies, a study was undertaken to measure the turbulent wind characteristics experienced by heavy-duty vehicles on the road. To measure the winds experienced on the road, a sport utility vehicle (SUV) was outfitted with an array of four fast-response pressure probes that could be arranged in vertical or horizontal rake configurations that provided measurements up to 4.0 m from the ground and spanning a width of 2.4 m. To characterize the influence of the proximity of the vehicle on the pressure signals of the probes, the SUV and its measurements system was calibrated in a large wind tunnel.

Turbulence is known to influence the aerodynamic and aeroacoustic performance of ground vehicles. What is not thoroughly understood are the characteristics of turbulence that influence this performance and how they can be applied in a consistent manner for aerodynamic design and evaluation purposes. Through collaboration between Transport Canada and the National Research Council Canada (NRC), a project was undertaken to develop a system for generating road-representative turbulence in the NRC 9 m Wind Tunnel, named the Road Turbulence System (RTS). This endeavour was undertaken in support of a larger project to evaluate new and emerging drag reduction technologies for heavy-duty vehicles. A multi-stage design process was used to develop the RTS for use with a 30% scale model of a heavy-duty vehicle in the NRC 9m Wind Tunnel.

Electric drives, whether in battery electric vehicles (BEVs) or various other applications, are an important part of modern transportation. Traditionally, physics-based models based on steady-state mapping of electric drives have been used to evaluate their behavior under transient conditions. Hardware-in-the-Loop (HIL) testing seeks to provide a more accurate representation of a component’s behavior under transient load conditions that are more representative of real world conditions it will operate under, without requiring a full vehicle installation. Oak Ridge National Laboratory (ORNL) developed such a HIL test platform capable of subjecting electric drives to both conventional steady-state test procedures as well as transient experiments such as vehicle drive cycles.

A truck platooning system was tested using two heavy-duty tractor-trailer trucks on a closed test track to investigate the sensitivity of intentional lateral offsets over a range of intervehicle spacings. The fuel consumption for both trucks in the platoon was measured using the SAE J1321 gravimetric procedure while travelling at 65 mph and loaded to a gross weight of 65,000 lb. In addition, the SAE J1939 instantaneous fuel rate was calibrated against the gravimetric measurements and used as proxy for additional analyses. The testing campaign demonstrated the effects of intervehicle gaps, following-vehicle longitudinal control, and manual lateral control. The new results are compared to previous truck-platooning studies to reinforce the value of the new information and demonstrate similarity to past trends. Fuel savings for the following vehicle was observed to exceed 10% at closer following distances.

Autonomous vehicles are currently a subject of great interest and there is heavy research on creating and improving algorithms for detecting objects in their vicinity. A ROS-based deep learning approach has been developed to detect objects using point cloud data. With encoded raw light detection and ranging (LiDAR) and camera data, several basic statistics such as elevation and density are generated. The system leverages a simple and fast convolutional neural network (CNN) solution for object identification and localization classification and generation of a bounding box to detect vehicles, pedestrians and cyclists was developed. The system is implemented on an Nvidia Jetson TX2 embedded computing platform, the classification and location of the objects are determined by the neural network. Coordinates and other properties of the object are published on to various ROS topics which are then serviced by visualization and data handling routines.

People through many years of experience, have developed a great intuitive sense for navigation and spatial awareness. With this intuition people are able to apply a near rules based approach to their driving. With a transition to autonomous driving, these intuitive skills need to be taught to the system which makes perception is the most fundamental and critical task. One of the major challenges for autonomous vehicles is accurately knowing the position of the vehicle relative to the world frame. Currently, this is achieved by utilizing expensive sensors such as a differential GPS which provides centimeter accuracy, or by using computationally taxing algorithms to attempt to match live input data from LiDARs or cameras to previously recorded data or maps. Within this paper an algorithm and accompanying hardware stack is proposed to reduce the computational load on the localization of the robot relative to a prior map.

The Friction Stir Spot Welding (FSSW) process is a derivative of the friction stir welding (FSW) process, without lateral movement of the tool during the welding process. It has been applied in the production of aluminum joining for various Mazda and Toyota vehicles. Most of the applications and published studies were concentrated in aluminum sheet in the range of 1.0 to 1.5 mm, suitable for non-structural automotive closure applications. The objective of this study is to study the feasibility of FSSW process for automotive structural aluminum joining, up to 3 mm in thickness, for potentially replacement of self-piercing rivets (SPR) process. Joining thicker aluminum with FSSW tooling with a typical smooth concave shoulder and threaded probing pin, requires long process time, which would not be appropriate in mass-production automotive body construction. In this paper, an innovative FSSW tool with grooved shoulder was developed.

Three established mechanical test specimen geometries and test methods used to evaluate mechanical properties of brittle materials are adapted to the diesel particulate filter (DPF) architecture to evaluate failure initiation stress and apparent elastic modulus of the ceramics comprising DPFs. The three custom-designed test coupons are harvested out of DPFs to promote a particular combination of orientation of crack initiation and crack plane. The testing of the DPF biaxial flexure disk produces a radial tensile stress and a crack plane parallel with the DPF's longitudinal axis. The testing of the DPF sectored flexural specimen produces axial tension at the DPF's OD and a crack plane perpendicular to the DPF's longitudinal axis. The testing of the DPF o-ring specimen produces hoop tension at the DPF's original outer diameter (OD) and at the inner diameter of the test coupon, and a crack plane parallel to the DPF's longitudinal axis.

Plug-in hybrid electric vehicles (PHEV) operate predominantly as electric vehicles (EV) with intermittent assist from the engine. As a consequence, the engine can be subjected to multiple cold start events. These cold start events have a significant impact on tailpipe emissions due to degraded catalyst performance and starting the engine under less than ideal conditions. On current conventional vehicles, the first cold start of the engine dictates whether or not the vehicle will pass federal emissions tests. PHEV operation compounds this problem due to infrequent, multiple engine cold starts. ORNL, in collaboration with the University of Tennessee, developed an Engine-In-the-Loop (EIL) test platform to investigate cold start emissions on a 2.0l Gasoline Turbocharged Direct Injection (GTDI) Ecotec engine coupled to a virtual series hybrid electric vehicle.

The 9-meter wind tunnel of the National Research Council (NRC) of Canada is commonly employed in testing of class 8 tractors at full- and model-scales. In support of this work a series of tests of an identical model at full- and half-scale were performed to investigate some of the effects resulting from simulation compromises. Minimum Reynolds Number considerations drive the crucial decisions of what scale and speed to employ for testing. The full- and half-scale campaigns included Reynolds Number sweeps allowing conclusions to be reached on the minimum Reynolds number required for testing of fully-detailed commercial truck models. Furthermore the Reynolds sweeps were repeated at a variety of yaw angles to examine whether the minimum Reynolds Number was a function of yaw angle and the resulting flow regime changes. The test section of the NRC 9-meter wind tunnel is not sufficiently long to accommodate a full-scale tractor and a typical trailer length of 48′ or more.

Current artificial intelligence techniques for end to end driving of autonomous vehicles typically rely on a single form of learning or training processes along with a corresponding dataset or simulation environment. Relatively speaking, success has been shown for a variety of learning modalities in which it can be shown that the machine can successfully “drive” a vehicle. However, the realm of real-world driving extends significantly beyond the realm of limited test environments for machine training. This creates an enormous gap in capability between these two realms. With their superior neural network structures and learning capabilities, humans can be easily trained within a short period of time to proceed from limited test environments to real world driving.

Saab Automobile recently released the BioPower engines, advertised to use increased turbocharger boost and spark advance on ethanol fuel to enhance performance. Specifications for the 2.0 liter turbocharged engine in the Saab 9-5 Biopower 2.0t report 150 hp (112 kW) on gasoline and a 20% increase to 180 hp (134 kW) on E85 (nominally 85% ethanol, 15% gasoline). While FFVs sold in the U.S. must be emissions certified on Federal Certification Gasoline as well as on E85, the European regulations only require certification on gasoline. Owing to renewed and growing interest in increased ethanol utilization in the U.S., a European-specification 2007 Saab 9-5 Biopower 2.0t was acquired by the Department of Energy and Oak Ridge National Laboratory (ORNL) for benchmark evaluations. Results show that the vehicle's gasoline equivalent fuel economy on the Federal Test Procedure (FTP) and the Highway Fuel Economy Test (HFET) are on par with similar U.S.-legal flex-fuel vehicles.

The effect of B and Si additions on fracture and fatigue performance of vacuum carburized 4320 steel and modifications of 4320 steel containing additions of Si (1.0 and 2.0 wt pct) and B (0 and 17 ppm) was evaluated by bending fatigue testing. Three rates of gas quenching, in 10 bar nitrogen and 15 and 20 bar helium, were used to cool specimens after carburizing. The B, protected by Ti additions, together with the Si additions, increased core hardenability. The B/Si modified steels showed no improvement in fatigue resistance, as measured by endurance limits established by 10 million cycle runouts without fracture. However, scanning electron microscopy showed that Si reduced sensitivity to intergranular fracture or quench embrittlement, a major cause of bending fatigue crack initiation, and contributed to variable fatigue performance, with both low-cycle failures and runout performance at applied stresses significantly above measured endurance limits.

The development of new catalytic materials is still dominated by trial and error methods, even though the experimental and theoretical bases for their characterization have improved dramatically in recent years. Although it has been successful, the empirical development of catalytic materials is time consuming and expensive with no guarantee of success. We have been exploring computationally complex but experimentally simple systems to establish a “catalysis by design” protocol that combines the power of theory and experiment. We hope to translate the fundamental insights directly into a complete catalyst system that is technologically relevant. The essential component of this approach is that the catalysts are iteratively examined by both theoretical and experimental methods.